The present invention relates generally to neuromorphic systems, and more specifically, to neuromorphic networks utilizing phase change devices.
Biological systems impose order on the information provided by their sensory input. This information typically comes in the form of spatiotemporal patterns comprising localized events with a distinctive spatial and temporal structure. These events occur on a wide variety of spatial and temporal scales, and yet a biological system such as the brain is still able to integrate them and extract relevant pieces of information. Such biological systems can rapidly extract signals from noisy spatiotemporal inputs.
In biological systems, the point of contact between an axon of a neuron and a dendrite on another neuron is called a synapse, with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic. The essence of our individual experiences is stored in the conductance of the synapses. The synaptic conductance can change with time as a function of the relative spike times of pre-synaptic and post-synaptic neurons, as per spike-timing dependent plasticity (STDP). The STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of the two firings is reversed.
Neuromorphic systems, also referred to as artificial neural networks, are computational systems that permit electronic systems to essentially function in a manner analogous to that of biological systems. Neuromorphic systems do not generally utilize the traditional digital model of manipulating 0s and 1s. Instead, neuromorphic systems create connections between processing elements that are roughly functionally equivalent to neurons of a biological brain. Neuromorphic systems may comprise various electronic circuits that are modeled on biological neurons.